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Found 14 papers, 4 papers with code
Coseparable Nonnegative Tensor Factorization With T-CUR Decomposition
This approach extends the coseparable NMF to the tensor setting, creating what we term coseparable Nonnegative Tensor Factorization (NTF).
On the Robustness of Cross-Concentrated Sampling for Matrix Completion
Matrix completion is one of the crucial tools in modern data science research.
Robust Tensor CUR Decompositions: Rapid Low-Tucker-Rank Tensor Recovery with Sparse Corruption
We study the tensor robust principal component analysis (TRPCA) problem, a tensorial extension of matrix robust principal component analysis (RPCA), that aims to split the given tensor into an underlying low-rank component and a sparse outlier component.
Non-convex approaches for low-rank tensor completion under tubal sampling
Tensor completion is an important problem in modern data analysis.
Randomized Kaczmarz in Adversarial Distributed Setting
Additionally, the efficiency of the proposed methods for solving convex problems is shown in simulations with the presence of adversaries.
Matrix Completion with Cross-Concentrated Sampling: Bridging Uniform Sampling and CUR Sampling
While uniform sampling has been widely studied in the matrix completion literature, CUR sampling approximates a low-rank matrix via row and column samples.
Distributed randomized Kaczmarz for the adversarial workers
Developing large-scale distributed methods that are robust to the presence of adversarial or corrupted workers is an important part of making such methods practical for real-world problems.
Guided Semi-Supervised Non-negative Matrix Factorization on Legal Documents
Classification and topic modeling are popular techniques in machine learning that extract information from large-scale datasets.
Fast Robust Tensor Principal Component Analysis via Fiber CUR Decomposition
We study the problem of tensor robust principal component analysis (TRPCA), which aims to separate an underlying low-multilinear-rank tensor and a sparse outlier tensor from their sum.
Mode-wise Tensor Decompositions: Multi-dimensional Generalizations of CUR Decompositions
Low rank tensor approximation is a fundamental tool in modern machine learning and data science.
Robust CUR Decomposition: Theory and Imaging Applications
Additionally, we consider hybrid randomized and deterministic sampling methods which produce a compact CUR decomposition of a given matrix, and apply this to video sequences to produce canonical frames thereof.
Rapid Robust Principal Component Analysis: CUR Accelerated Inexact Low Rank Estimation
Robust principal component analysis (RPCA) is a widely used tool for dimension reduction.
COVID-19 Literature Topic-Based Search via Hierarchical NMF
A dataset of COVID-19-related scientific literature is compiled, combining the articles from several online libraries and selecting those with open access and full text available.
CUR Decompositions, Approximations, and Perturbations
This article discusses a useful tool in dimensionality reduction and low-rank matrix approximation called the CUR decomposition.
